Overview

Dataset statistics

Number of variables32
Number of observations117963
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.6 MiB
Average record size in memory183.1 B

Variable types

Numeric15
Categorical16
DateTime1

Alerts

country has a high cardinality: 177 distinct values High cardinality
arrival_date_year is highly correlated with arrival_date_month and 1 other fieldsHigh correlation
arrival_date_month is highly correlated with arrival_date_year and 1 other fieldsHigh correlation
arrival_date_week_number is highly correlated with arrival_date_year and 1 other fieldsHigh correlation
stays_in_weekend_nights is highly correlated with stayHigh correlation
stays_in_week_nights is highly correlated with stayHigh correlation
is_repeated_guest is highly correlated with previous_bookings_not_canceledHigh correlation
previous_bookings_not_canceled is highly correlated with is_repeated_guestHigh correlation
stay is highly correlated with stays_in_weekend_nights and 1 other fieldsHigh correlation
arrival_date_year is highly correlated with arrival_date_month and 1 other fieldsHigh correlation
arrival_date_month is highly correlated with arrival_date_year and 1 other fieldsHigh correlation
arrival_date_week_number is highly correlated with arrival_date_year and 1 other fieldsHigh correlation
stays_in_weekend_nights is highly correlated with stayHigh correlation
stays_in_week_nights is highly correlated with stayHigh correlation
stay is highly correlated with stays_in_weekend_nights and 1 other fieldsHigh correlation
arrival_date_month is highly correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly correlated with arrival_date_monthHigh correlation
stays_in_weekend_nights is highly correlated with stayHigh correlation
stays_in_week_nights is highly correlated with stayHigh correlation
is_repeated_guest is highly correlated with previous_bookings_not_canceledHigh correlation
previous_bookings_not_canceled is highly correlated with is_repeated_guestHigh correlation
stay is highly correlated with stays_in_weekend_nights and 1 other fieldsHigh correlation
is_canceled is highly correlated with reservation_statusHigh correlation
reservation_status is highly correlated with is_canceledHigh correlation
distribution_channel is highly correlated with market_segmentHigh correlation
reserved_room_type is highly correlated with assigned_room_typeHigh correlation
assigned_room_type is highly correlated with reserved_room_typeHigh correlation
market_segment is highly correlated with distribution_channelHigh correlation
df_index is highly correlated with hotel and 6 other fieldsHigh correlation
hotel is highly correlated with df_indexHigh correlation
is_canceled is highly correlated with df_index and 1 other fieldsHigh correlation
arrival_date_year is highly correlated with df_index and 3 other fieldsHigh correlation
arrival_date_month is highly correlated with df_index and 3 other fieldsHigh correlation
arrival_date_week_number is highly correlated with df_index and 2 other fieldsHigh correlation
stays_in_weekend_nights is highly correlated with stays_in_week_nights and 1 other fieldsHigh correlation
stays_in_week_nights is highly correlated with stays_in_weekend_nights and 1 other fieldsHigh correlation
children is highly correlated with reserved_room_type and 1 other fieldsHigh correlation
market_segment is highly correlated with distribution_channelHigh correlation
distribution_channel is highly correlated with market_segmentHigh correlation
previous_cancellations is highly correlated with previous_bookings_not_canceledHigh correlation
previous_bookings_not_canceled is highly correlated with previous_cancellationsHigh correlation
reserved_room_type is highly correlated with children and 2 other fieldsHigh correlation
assigned_room_type is highly correlated with children and 1 other fieldsHigh correlation
deposit_type is highly correlated with reservation_statusHigh correlation
adr is highly correlated with arrival_date_month and 1 other fieldsHigh correlation
reservation_status is highly correlated with df_index and 2 other fieldsHigh correlation
reservation_status_date is highly correlated with df_index and 1 other fieldsHigh correlation
stay is highly correlated with stays_in_weekend_nights and 1 other fieldsHigh correlation
previous_cancellations is highly skewed (γ1 = 24.33573338) Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 24.00883198) Skewed
df_index has unique values Unique
lead_time has 5912 (5.0%) zeros Zeros
stays_in_weekend_nights has 50976 (43.2%) zeros Zeros
stays_in_week_nights has 6892 (5.8%) zeros Zeros
previous_cancellations has 111526 (94.5%) zeros Zeros
previous_bookings_not_canceled has 114601 (97.1%) zeros Zeros
booking_changes has 100169 (84.9%) zeros Zeros
days_in_waiting_list has 114267 (96.9%) zeros Zeros
adr has 1227 (1.0%) zeros Zeros
total_of_special_requests has 69417 (58.8%) zeros Zeros

Reproduction

Analysis started2022-02-07 07:16:14.208126
Analysis finished2022-02-07 07:16:56.666686
Duration42.46 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct117963
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59963.03778
Minimum2
Maximum119389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:56.723264image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6012.1
Q130352.5
median60053
Q389709.5
95-th percentile113480.9
Maximum119389
Range119387
Interquartile range (IQR)59357

Descriptive statistics

Standard deviation34387.63698
Coefficient of variation (CV)0.5734805681
Kurtosis-1.190833738
Mean59963.03778
Median Absolute Deviation (MAD)29679
Skewness-0.009467411338
Sum7073419826
Variance1182509577
MonotonicityStrictly increasing
2022-02-07T02:16:56.825221image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21
 
< 0.1%
797751
 
< 0.1%
797731
 
< 0.1%
797721
 
< 0.1%
797711
 
< 0.1%
797701
 
< 0.1%
797691
 
< 0.1%
797681
 
< 0.1%
797671
 
< 0.1%
797661
 
< 0.1%
Other values (117953)117953
> 99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
1193891
< 0.1%
1193881
< 0.1%
1193871
< 0.1%
1193861
< 0.1%
1193851
< 0.1%
1193841
< 0.1%
1193831
< 0.1%
1193821
< 0.1%
1193811
< 0.1%
1193801
< 0.1%

hotel
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.4 KiB
City Hotel
78912 
Resort Hotel
39051 

Length

Max length12
Median length10
Mean length10.66208896
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel78912
66.9%
Resort Hotel39051
33.1%

Length

2022-02-07T02:16:56.923242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:56.982177image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
hotel117963
50.0%
city78912
33.4%
resort39051
 
16.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

is_canceled
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size921.7 KiB
0
73959 
1
44004 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
073959
62.7%
144004
37.3%

Length

2022-02-07T02:16:57.032342image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:57.082017image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
073959
62.7%
144004
37.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lead_time
Real number (ℝ≥0)

ZEROS

Distinct476
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.7351627
Minimum0
Maximum629
Zeros5912
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:57.142282image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median70
Q3161
95-th percentile320
Maximum629
Range629
Interquartile range (IQR)142

Descriptive statistics

Standard deviation106.9707854
Coefficient of variation (CV)1.021345483
Kurtosis1.667113783
Mean104.7351627
Median Absolute Deviation (MAD)60
Skewness1.339451744
Sum12354874
Variance11442.74893
MonotonicityNot monotonic
2022-02-07T02:16:57.243601image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05912
 
5.0%
13330
 
2.8%
22008
 
1.7%
31788
 
1.5%
41683
 
1.4%
51538
 
1.3%
61408
 
1.2%
71301
 
1.1%
81116
 
0.9%
121068
 
0.9%
Other values (466)96811
82.1%
ValueCountFrequency (%)
05912
5.0%
13330
2.8%
22008
 
1.7%
31788
 
1.5%
41683
 
1.4%
51538
 
1.3%
61408
 
1.2%
71301
 
1.1%
81116
 
0.9%
9975
 
0.8%
ValueCountFrequency (%)
62917
< 0.1%
62630
< 0.1%
62217
< 0.1%
61517
< 0.1%
60817
< 0.1%
60530
< 0.1%
60117
< 0.1%
59417
< 0.1%
58717
< 0.1%
58017
< 0.1%

arrival_date_year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size921.7 KiB
2016
55936 
2017
40352 
2015
21675 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
201655936
47.4%
201740352
34.2%
201521675
 
18.4%

Length

2022-02-07T02:16:57.334631image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:57.389138image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
201655936
47.4%
201740352
34.2%
201521675
 
18.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

arrival_date_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.555513169
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size460.9 KiB
2022-02-07T02:16:57.437047image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.081294038
Coefficient of variation (CV)0.4700309432
Kurtosis-0.9873507447
Mean6.555513169
Median Absolute Deviation (MAD)2
Skewness-0.02876489093
Sum773308
Variance9.494372951
MonotonicityNot monotonic
2022-02-07T02:16:57.504149image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
813796
11.7%
712560
10.6%
511698
9.9%
411007
9.3%
1010993
9.3%
610879
9.2%
910435
8.8%
39601
8.1%
27908
6.7%
116666
5.7%
Other values (2)12420
10.5%
ValueCountFrequency (%)
15782
4.9%
27908
6.7%
39601
8.1%
411007
9.3%
511698
9.9%
610879
9.2%
712560
10.6%
813796
11.7%
910435
8.8%
1010993
9.3%
ValueCountFrequency (%)
126638
5.6%
116666
5.7%
1010993
9.3%
910435
8.8%
813796
11.7%
712560
10.6%
610879
9.2%
511698
9.9%
411007
9.3%
39601
8.1%

arrival_date_week_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.17869162
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:57.593168image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.56348511
Coefficient of variation (CV)0.4990484936
Kurtosis-0.9787054744
Mean27.17869162
Median Absolute Deviation (MAD)11
Skewness-0.01082883282
Sum3206080
Variance183.9681284
MonotonicityNot monotonic
2022-02-07T02:16:57.693376image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333557
 
3.0%
303062
 
2.6%
343027
 
2.6%
323024
 
2.6%
182894
 
2.5%
282829
 
2.4%
212811
 
2.4%
172800
 
2.4%
202773
 
2.4%
292746
 
2.3%
Other values (43)88440
75.0%
ValueCountFrequency (%)
11029
0.9%
21193
1.0%
31275
1.1%
41447
1.2%
51352
1.1%
61466
1.2%
72066
1.8%
82189
1.9%
92038
1.7%
102105
1.8%
ValueCountFrequency (%)
531798
1.5%
521165
1.0%
51890
0.8%
501469
1.2%
491761
1.5%
481469
1.2%
471659
1.4%
461538
1.3%
451911
1.6%
442220
1.9%

arrival_date_day_of_month
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.81058467
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:57.782381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.778873094
Coefficient of variation (CV)0.5552529065
Kurtosis-1.187186386
Mean15.81058467
Median Absolute Deviation (MAD)8
Skewness-0.003431509956
Sum1865064
Variance77.06861281
MonotonicityNot monotonic
2022-02-07T02:16:57.858668image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
174330
 
3.7%
54264
 
3.6%
154124
 
3.5%
254118
 
3.5%
264102
 
3.5%
94053
 
3.4%
124050
 
3.4%
164035
 
3.4%
194025
 
3.4%
24010
 
3.4%
Other values (21)76852
65.1%
ValueCountFrequency (%)
13545
3.0%
24010
3.4%
33807
3.2%
43703
3.1%
54264
3.6%
63784
3.2%
73623
3.1%
83864
3.3%
94053
3.4%
103529
3.0%
ValueCountFrequency (%)
312189
1.9%
303809
3.2%
293543
3.0%
283903
3.3%
273758
3.2%
264102
3.5%
254118
3.5%
243951
3.3%
233577
3.0%
223563
3.0%

stays_in_weekend_nights
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9219924892
Minimum0
Maximum6
Zeros50976
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:57.929971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.953329183
Coefficient of variation (CV)1.033988014
Kurtosis0.2219074172
Mean0.9219924892
Median Absolute Deviation (MAD)1
Skewness0.7471127314
Sum108761
Variance0.9088365311
MonotonicityNot monotonic
2022-02-07T02:16:57.989625image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
050976
43.2%
233249
28.2%
130526
25.9%
41849
 
1.6%
31253
 
1.1%
578
 
0.1%
632
 
< 0.1%
ValueCountFrequency (%)
050976
43.2%
130526
25.9%
233249
28.2%
31253
 
1.1%
41849
 
1.6%
578
 
0.1%
632
 
< 0.1%
ValueCountFrequency (%)
632
 
< 0.1%
578
 
0.1%
41849
 
1.6%
31253
 
1.1%
233249
28.2%
130526
25.9%
050976
43.2%

stays_in_week_nights
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.485075829
Minimum0
Maximum15
Zeros6892
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:58.062539image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.743486635
Coefficient of variation (CV)0.7015828712
Kurtosis4.03256695
Mean2.485075829
Median Absolute Deviation (MAD)1
Skewness1.513946426
Sum293147
Variance3.039745647
MonotonicityNot monotonic
2022-02-07T02:16:58.130940image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
233574
28.5%
130088
25.5%
322203
18.8%
511051
 
9.4%
49554
 
8.1%
06892
 
5.8%
61491
 
1.3%
101030
 
0.9%
71027
 
0.9%
8654
 
0.6%
Other values (6)399
 
0.3%
ValueCountFrequency (%)
06892
 
5.8%
130088
25.5%
233574
28.5%
322203
18.8%
49554
 
8.1%
511051
 
9.4%
61491
 
1.3%
71027
 
0.9%
8654
 
0.6%
9231
 
0.2%
ValueCountFrequency (%)
159
 
< 0.1%
1435
 
< 0.1%
1327
 
< 0.1%
1242
 
< 0.1%
1155
 
< 0.1%
101030
0.9%
9231
 
0.2%
8654
0.6%
71027
0.9%
61491
1.3%

adults
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.860362995
Minimum0
Maximum55
Zeros325
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:58.204388image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5775354861
Coefficient of variation (CV)0.310442364
Kurtosis1384.363683
Mean1.860362995
Median Absolute Deviation (MAD)0
Skewness18.72070574
Sum219454
Variance0.3335472377
MonotonicityNot monotonic
2022-02-07T02:16:58.268166image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
288895
75.4%
122487
 
19.1%
36178
 
5.2%
0325
 
0.3%
462
 
0.1%
265
 
< 0.1%
272
 
< 0.1%
202
 
< 0.1%
52
 
< 0.1%
401
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
0325
 
0.3%
122487
 
19.1%
288895
75.4%
36178
 
5.2%
462
 
0.1%
52
 
< 0.1%
61
 
< 0.1%
101
 
< 0.1%
202
 
< 0.1%
265
 
< 0.1%
ValueCountFrequency (%)
551
 
< 0.1%
501
 
< 0.1%
401
 
< 0.1%
272
 
< 0.1%
265
 
< 0.1%
202
 
< 0.1%
101
 
< 0.1%
61
 
< 0.1%
52
 
< 0.1%
462
0.1%

children
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size921.7 KiB
0
109427 
1
 
4830
2
 
3629
3
 
76
10
 
1

Length

Max length2
Median length1
Mean length1.000008477
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0109427
92.8%
14830
 
4.1%
23629
 
3.1%
376
 
0.1%
101
 
< 0.1%

Length

2022-02-07T02:16:58.344620image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:58.397881image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0109427
92.8%
14830
 
4.1%
23629
 
3.1%
376
 
0.1%
101
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

babies
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size921.7 KiB
0
117054 
1
 
893
2
 
14
10
 
1
9
 
1

Length

Max length2
Median length1
Mean length1.000008477
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0117054
99.2%
1893
 
0.8%
214
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Length

2022-02-07T02:16:58.457909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:58.512971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0117054
99.2%
1893
 
0.8%
214
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

meal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.5 KiB
BB
91202 
HB
14270 
SC
10544 
Undefined
 
1150
FB
 
797

Length

Max length9
Median length2
Mean length2.068241737
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB91202
77.3%
HB14270
 
12.1%
SC10544
 
8.9%
Undefined1150
 
1.0%
FB797
 
0.7%

Length

2022-02-07T02:16:58.575166image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:58.631068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
bb91202
77.3%
hb14270
 
12.1%
sc10544
 
8.9%
undefined1150
 
1.0%
fb797
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

country
Categorical

HIGH CARDINALITY

Distinct177
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size236.0 KiB
PRT
47892 
GBR
12072 
FRA
10389 
ESP
8550 
DEU
7276 
Other values (172)
31784 

Length

Max length3
Median length3
Mean length2.989310207
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowGBR
2nd rowGBR
3rd rowGBR
4th rowGBR
5th rowPRT

Common Values

ValueCountFrequency (%)
PRT47892
40.6%
GBR12072
 
10.2%
FRA10389
 
8.8%
ESP8550
 
7.2%
DEU7276
 
6.2%
ITA3759
 
3.2%
IRL3372
 
2.9%
BEL2334
 
2.0%
BRA2216
 
1.9%
NLD2097
 
1.8%
Other values (167)18006
 
15.3%

Length

2022-02-07T02:16:58.692366image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt47892
40.6%
gbr12072
 
10.2%
fra10389
 
8.8%
esp8550
 
7.2%
deu7276
 
6.2%
ita3759
 
3.2%
irl3372
 
2.9%
bel2334
 
2.0%
bra2216
 
1.9%
nld2097
 
1.8%
Other values (167)18006
 
15.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

market_segment
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Online TA
56004 
Offline TA/TO
23957 
Groups
19714 
Direct
12290 
Corporate
 
5050
Other values (2)
 
948

Length

Max length13
Median length9
Mean length9.020794656
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowCorporate
3rd rowOnline TA
4th rowOnline TA
5th rowDirect

Common Values

ValueCountFrequency (%)
Online TA56004
47.5%
Offline TA/TO23957
20.3%
Groups19714
 
16.7%
Direct12290
 
10.4%
Corporate5050
 
4.3%
Complementary717
 
0.6%
Aviation231
 
0.2%

Length

2022-02-07T02:16:58.769093image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:58.827924image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
online56004
28.3%
ta56004
28.3%
offline23957
12.1%
ta/to23957
12.1%
groups19714
 
10.0%
direct12290
 
6.2%
corporate5050
 
2.6%
complementary717
 
0.4%
aviation231
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

distribution_channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.5 KiB
TA/TO
97074 
Direct
14294 
Corporate
 
6405
GDS
 
189
Undefined
 
1

Length

Max length9
Median length5
Mean length5.335189848
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDirect
2nd rowCorporate
3rd rowTA/TO
4th rowTA/TO
5th rowDirect

Common Values

ValueCountFrequency (%)
TA/TO97074
82.3%
Direct14294
 
12.1%
Corporate6405
 
5.4%
GDS189
 
0.2%
Undefined1
 
< 0.1%

Length

2022-02-07T02:16:58.902470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:58.953923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
ta/to97074
82.3%
direct14294
 
12.1%
corporate6405
 
5.4%
gds189
 
0.2%
undefined1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

is_repeated_guest
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size921.7 KiB
0
114466 
1
 
3497

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0114466
97.0%
13497
 
3.0%

Length

2022-02-07T02:16:59.013094image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:59.370826image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0114466
97.0%
13497
 
3.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

previous_cancellations
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08773089867
Minimum0
Maximum26
Zeros111526
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:59.413141image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8491095557
Coefficient of variation (CV)9.678568994
Kurtosis666.9137839
Mean0.08773089867
Median Absolute Deviation (MAD)0
Skewness24.33573338
Sum10349
Variance0.7209870376
MonotonicityNot monotonic
2022-02-07T02:16:59.484825image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0111526
94.5%
16009
 
5.1%
2111
 
0.1%
365
 
0.1%
2448
 
< 0.1%
1135
 
< 0.1%
431
 
< 0.1%
2626
 
< 0.1%
2525
 
< 0.1%
622
 
< 0.1%
Other values (5)65
 
0.1%
ValueCountFrequency (%)
0111526
94.5%
16009
 
5.1%
2111
 
0.1%
365
 
0.1%
431
 
< 0.1%
519
 
< 0.1%
622
 
< 0.1%
1135
 
< 0.1%
1312
 
< 0.1%
1414
 
< 0.1%
ValueCountFrequency (%)
2626
< 0.1%
2525
< 0.1%
2448
< 0.1%
211
 
< 0.1%
1919
 
< 0.1%
1414
 
< 0.1%
1312
 
< 0.1%
1135
< 0.1%
622
< 0.1%
519
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1318803354
Minimum0
Maximum72
Zeros114601
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:59.570751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.488793064
Coefficient of variation (CV)11.28896935
Kurtosis791.8969061
Mean0.1318803354
Median Absolute Deviation (MAD)0
Skewness24.00883198
Sum15557
Variance2.216504786
MonotonicityNot monotonic
2022-02-07T02:16:59.662252image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0114601
97.1%
11432
 
1.2%
2531
 
0.5%
3299
 
0.3%
4206
 
0.2%
5169
 
0.1%
6109
 
0.1%
784
 
0.1%
867
 
0.1%
957
 
< 0.1%
Other values (63)408
 
0.3%
ValueCountFrequency (%)
0114601
97.1%
11432
 
1.2%
2531
 
0.5%
3299
 
0.3%
4206
 
0.2%
5169
 
0.1%
6109
 
0.1%
784
 
0.1%
867
 
0.1%
957
 
< 0.1%
ValueCountFrequency (%)
721
< 0.1%
711
< 0.1%
701
< 0.1%
691
< 0.1%
681
< 0.1%
671
< 0.1%
661
< 0.1%
651
< 0.1%
641
< 0.1%
631
< 0.1%

reserved_room_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
A
84944 
D
19037 
E
 
6418
F
 
2870
G
 
2060
Other values (4)
 
2634

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowC

Common Values

ValueCountFrequency (%)
A84944
72.0%
D19037
 
16.1%
E6418
 
5.4%
F2870
 
2.4%
G2060
 
1.7%
B1109
 
0.9%
C922
 
0.8%
H597
 
0.5%
L6
 
< 0.1%

Length

2022-02-07T02:16:59.753816image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:16:59.808623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
a84944
72.0%
d19037
 
16.1%
e6418
 
5.4%
f2870
 
2.4%
g2060
 
1.7%
b1109
 
0.9%
c922
 
0.8%
h597
 
0.5%
l6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

assigned_room_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
A
73500 
D
25011 
E
7665 
F
 
3701
G
 
2511
Other values (6)
 
5575

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowA
3rd rowA
4th rowA
5th rowC

Common Values

ValueCountFrequency (%)
A73500
62.3%
D25011
 
21.2%
E7665
 
6.5%
F3701
 
3.1%
G2511
 
2.1%
C2328
 
2.0%
B2146
 
1.8%
H704
 
0.6%
I213
 
0.2%
K183
 
0.2%

Length

2022-02-07T02:16:59.881229image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a73500
62.3%
d25011
 
21.2%
e7665
 
6.5%
f3701
 
3.1%
g2511
 
2.1%
c2328
 
2.0%
b2146
 
1.8%
h704
 
0.6%
i213
 
0.2%
k183
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

booking_changes
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2192721447
Minimum0
Maximum18
Zeros100169
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:16:59.948734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6386015147
Coefficient of variation (CV)2.912369538
Kurtosis59.37769424
Mean0.2192721447
Median Absolute Deviation (MAD)0
Skewness5.374868144
Sum25866
Variance0.4078118946
MonotonicityNot monotonic
2022-02-07T02:17:00.017954image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0100169
84.9%
112508
 
10.6%
23749
 
3.2%
3918
 
0.8%
4370
 
0.3%
5113
 
0.1%
660
 
0.1%
729
 
< 0.1%
817
 
< 0.1%
98
 
< 0.1%
Other values (9)22
 
< 0.1%
ValueCountFrequency (%)
0100169
84.9%
112508
 
10.6%
23749
 
3.2%
3918
 
0.8%
4370
 
0.3%
5113
 
0.1%
660
 
0.1%
729
 
< 0.1%
817
 
< 0.1%
98
 
< 0.1%
ValueCountFrequency (%)
181
 
< 0.1%
171
 
< 0.1%
162
 
< 0.1%
153
 
< 0.1%
143
 
< 0.1%
134
< 0.1%
121
 
< 0.1%
112
 
< 0.1%
105
< 0.1%
98
< 0.1%

deposit_type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.5 KiB
No Deposit
103249 
Non Refund
14552 
Refundable
 
162

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit103249
87.5%
Non Refund14552
 
12.3%
Refundable162
 
0.1%

Length

2022-02-07T02:17:00.099882image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:17:00.147278image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
no103249
43.8%
deposit103249
43.8%
non14552
 
6.2%
refund14552
 
6.2%
refundable162
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

days_in_waiting_list
Real number (ℝ≥0)

ZEROS

Distinct127
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.348083721
Minimum0
Maximum391
Zeros114267
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:17:00.213690image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.69648405
Coefficient of variation (CV)7.536564343
Kurtosis184.6289963
Mean2.348083721
Median Absolute Deviation (MAD)0
Skewness11.87555273
Sum276987
Variance313.1655477
MonotonicityNot monotonic
2022-02-07T02:17:00.308994image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0114267
96.9%
39227
 
0.2%
58164
 
0.1%
44141
 
0.1%
31127
 
0.1%
3596
 
0.1%
4694
 
0.1%
6989
 
0.1%
6383
 
0.1%
5080
 
0.1%
Other values (117)2595
 
2.2%
ValueCountFrequency (%)
0114267
96.9%
112
 
< 0.1%
25
 
< 0.1%
359
 
0.1%
425
 
< 0.1%
58
 
< 0.1%
616
 
< 0.1%
74
 
< 0.1%
87
 
< 0.1%
916
 
< 0.1%
ValueCountFrequency (%)
39145
< 0.1%
37915
 
< 0.1%
33015
 
< 0.1%
25910
 
< 0.1%
23635
< 0.1%
22410
 
< 0.1%
22361
0.1%
21521
 
< 0.1%
20715
 
< 0.1%
1931
 
< 0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.5 KiB
Transient
88410 
Transient-Party
24945 
Contract
 
4043
Group
 
565

Length

Max length15
Median length9
Mean length10.21535566
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient88410
74.9%
Transient-Party24945
 
21.1%
Contract4043
 
3.4%
Group565
 
0.5%

Length

2022-02-07T02:17:00.399860image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:17:00.447152image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
transient88410
74.9%
transient-party24945
 
21.1%
contract4043
 
3.4%
group565
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adr
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct8832
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.6252728
Minimum-6.38
Maximum451.5
Zeros1227
Zeros (%)1.0%
Negative1
Negative (%)< 0.1%
Memory size921.7 KiB
2022-02-07T02:17:00.514937image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile40
Q170
median95
Q3126
95-th percentile194
Maximum451.5
Range457.88
Interquartile range (IQR)56

Descriptive statistics

Standard deviation47.55466702
Coefficient of variation (CV)0.4633816379
Kurtosis2.141034062
Mean102.6252728
Median Absolute Deviation (MAD)27.5
Skewness1.071656617
Sum12105985.06
Variance2261.446355
MonotonicityNot monotonic
2022-02-07T02:17:00.609099image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
623751
 
3.2%
752710
 
2.3%
902471
 
2.1%
652394
 
2.0%
801887
 
1.6%
951660
 
1.4%
1201607
 
1.4%
1001573
 
1.3%
851537
 
1.3%
1101523
 
1.3%
Other values (8822)96850
82.1%
ValueCountFrequency (%)
-6.381
 
< 0.1%
01227
1.0%
0.51
 
< 0.1%
115
 
< 0.1%
1.291
 
< 0.1%
1.481
 
< 0.1%
1.562
 
< 0.1%
1.61
 
< 0.1%
1.81
 
< 0.1%
212
 
< 0.1%
ValueCountFrequency (%)
451.51
< 0.1%
4501
< 0.1%
4371
< 0.1%
426.251
< 0.1%
4021
< 0.1%
397.381
< 0.1%
3922
< 0.1%
3882
< 0.1%
3871
< 0.1%
3841
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size921.7 KiB
0
110678 
1
 
7254
2
 
26
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110678
93.8%
17254
 
6.1%
226
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Length

2022-02-07T02:17:00.693916image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:17:00.743638image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0110678
93.8%
17254
 
6.1%
226
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

total_of_special_requests
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5722302756
Minimum0
Maximum5
Zeros69417
Zeros (%)58.8%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:17:00.792344image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7931765082
Coefficient of variation (CV)1.386114196
Kurtosis1.47745868
Mean0.5722302756
Median Absolute Deviation (MAD)0
Skewness1.345989519
Sum67502
Variance0.6291289732
MonotonicityNot monotonic
2022-02-07T02:17:00.858114image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
069417
58.8%
132851
27.8%
212848
 
10.9%
32471
 
2.1%
4338
 
0.3%
538
 
< 0.1%
ValueCountFrequency (%)
069417
58.8%
132851
27.8%
212848
 
10.9%
32471
 
2.1%
4338
 
0.3%
538
 
< 0.1%
ValueCountFrequency (%)
538
 
< 0.1%
4338
 
0.3%
32471
 
2.1%
212848
 
10.9%
132851
27.8%
069417
58.8%

reservation_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.5 KiB
Check-Out
73959 
Canceled
42825 
No-Show
 
1179

Length

Max length9
Median length9
Mean length8.616973119
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out73959
62.7%
Canceled42825
36.3%
No-Show1179
 
1.0%

Length

2022-02-07T02:17:00.935387image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-07T02:17:00.994154image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
check-out73959
62.7%
canceled42825
36.3%
no-show1179
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reservation_status_date
Date

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size921.7 KiB
Minimum2014-01-01 00:00:00
Maximum2017-01-01 00:00:00
2022-02-07T02:17:01.040259image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:17:01.104614image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

stay
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.407068318
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size921.7 KiB
2022-02-07T02:17:01.175588image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.317633401
Coefficient of variation (CV)0.6802427144
Kurtosis5.261663905
Mean3.407068318
Median Absolute Deviation (MAD)1
Skewness1.866688871
Sum401908
Variance5.371424581
MonotonicityNot monotonic
2022-02-07T02:17:01.248304image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
227529
23.3%
327025
22.9%
120793
17.6%
417363
14.7%
78633
 
7.3%
57765
 
6.6%
63850
 
3.3%
81157
 
1.0%
101135
 
1.0%
14913
 
0.8%
Other values (10)1800
 
1.5%
ValueCountFrequency (%)
120793
17.6%
227529
23.3%
327025
22.9%
417363
14.7%
57765
 
6.6%
63850
 
3.3%
78633
 
7.3%
81157
 
1.0%
9838
 
0.7%
101135
 
1.0%
ValueCountFrequency (%)
2014
 
< 0.1%
1922
 
< 0.1%
1835
 
< 0.1%
1720
 
< 0.1%
1640
 
< 0.1%
1573
 
0.1%
14913
0.8%
13140
 
0.1%
12223
 
0.2%
11395
0.3%

Interactions

2022-02-07T02:16:53.396978image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:27.225946image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:29.226239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:31.087953image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:33.052479image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:34.909326image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:36.797629image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:38.810324image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:40.727347image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:42.473981image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:44.343227image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:46.058952image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:47.803474image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:49.538038image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:51.539030image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:53.532500image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:27.353107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:29.351213image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:31.208396image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:33.174781image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:35.034851image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:36.921389image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:38.943923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:40.846099image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:42.586263image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:44.459261image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:46.175073image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:47.916186image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:49.653207image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:51.664144image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:53.664005image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:27.476952image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:29.475133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:31.327976image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:33.299041image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:35.159401image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:37.041697image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:39.075697image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:40.962271image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:42.698833image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:44.575104image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:46.291897image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:48.032271image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:49.769189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:51.782598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:53.791133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:27.598043image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:29.597876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:31.447316image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:33.416904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:35.283877image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:37.165054image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:39.206373image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:41.075624image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:42.812501image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:44.687623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:46.407709image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:48.144324image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:49.885748image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:51.907201image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:53.918153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:27.721239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:29.718998image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:31.568489image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:33.539507image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:35.408426image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:37.285659image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:39.333078image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:41.190233image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:42.924698image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:44.798508image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:46.520604image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:48.257800image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:49.998591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:52.033094image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:54.046089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:27.977215image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:29.842865image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:31.688968image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:33.663774image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-07T02:16:35.533769image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2022-02-07T02:17:01.339925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-07T02:17:01.554830image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-07T02:17:01.765848image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-07T02:17:01.971378image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-07T02:17:02.155662image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-07T02:16:55.377956image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-07T02:16:56.202596image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexhotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typedays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datestay
02Resort Hotel072015727101100BBGBRDirectDirect000AC0No Deposit0Transient75.000Check-Out2015-01-011
13Resort Hotel0132015727101100BBGBRCorporateCorporate000AA0No Deposit0Transient75.000Check-Out2015-01-011
24Resort Hotel0142015727102200BBGBROnline TATA/TO000AA0No Deposit0Transient98.001Check-Out2015-01-012
35Resort Hotel0142015727102200BBGBROnline TATA/TO000AA0No Deposit0Transient98.001Check-Out2015-01-012
46Resort Hotel002015727102200BBPRTDirectDirect000CC0No Deposit0Transient107.000Check-Out2015-01-012
57Resort Hotel092015727102200FBPRTDirectDirect000CC0No Deposit0Transient103.001Check-Out2015-01-012
68Resort Hotel1852015727103200BBPRTOnline TATA/TO000AA0No Deposit0Transient82.001Canceled2015-01-013
79Resort Hotel1752015727103200HBPRTOffline TA/TOTA/TO000DD0No Deposit0Transient105.500Canceled2015-01-013
810Resort Hotel1232015727104200BBPRTOnline TATA/TO000EE0No Deposit0Transient123.000Canceled2015-01-014
911Resort Hotel0352015727104200HBPRTOnline TATA/TO000DD0No Deposit0Transient145.000Check-Out2015-01-014

Last rows

df_indexhotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typedays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datestay
117953119380City Hotel04420178353113200SCDEUOnline TATA/TO000AA0No Deposit0Transient140.7501Check-Out2017-01-014
117954119381City Hotel018820178353123200BBDEUDirectDirect000AA0No Deposit0Transient99.0000Check-Out2017-01-015
117955119382City Hotel013520178353024300BBJPNOnline TATA/TO000GG0No Deposit0Transient209.0000Check-Out2017-01-016
117956119383City Hotel016420178353124200BBDEUOffline TA/TOTA/TO000AA0No Deposit0Transient87.6000Check-Out2017-01-016
117957119384City Hotel02120178353025200BBBELOffline TA/TOTA/TO000AA0No Deposit0Transient96.1402Check-Out2017-01-017
117958119385City Hotel02320178353025200BBBELOffline TA/TOTA/TO000AA0No Deposit0Transient96.1400Check-Out2017-01-017
117959119386City Hotel010220178353125300BBFRAOnline TATA/TO000EE0No Deposit0Transient225.4302Check-Out2017-01-017
117960119387City Hotel03420178353125200BBDEUOnline TATA/TO000DD0No Deposit0Transient157.7104Check-Out2017-01-017
117961119388City Hotel010920178353125200BBGBROnline TATA/TO000AA0No Deposit0Transient104.4000Check-Out2017-01-017
117962119389City Hotel020520178352927200HBDEUOnline TATA/TO000AA0No Deposit0Transient151.2002Check-Out2017-01-019